reg to calssic

Adding button

adding secrets

Adding some database support

chaning for to while for lineup add

Update salary input fields in Manage Portfolio section to allow for a wider range of values, enhancing user flexibility in setting exposure parameters.

Update salary field references in exposure_spread.py to ensure consistency in salary eligibility checks for player replacements.

adjusting salary declaration in exposure increase

Add update-pm-files.ps1 script for EC2 Instance Connect file updates and enhance exposure_spread.py with salary eligibility checks for player replacements

Lets try this

checking again

checking to see if this fixes the exposure increase issue

Refactor SE Score calculation in predict_dupes function to adjust the order of operations, ensuring player diversity is factored in after the score computation. This change enhances the accuracy of the SE Score while maintaining code clarity.

Refactor SE Score calculation in predict_dupes function to incorporate player diversity, enhancing the accuracy of the score. Removed redundant print statement for cleaner output.

Update excluded_cols list in app.py to include 'SE Score' for improved data handling in portfolio management.

Add print statement to display the top 10 SE Scores in predict_dupes function for debugging purposes

Adding SE score

ping

Refactor exposure_spread function to improve player replacement logic by implementing a while loop for better handling of row selections and ensuring replacements are made only when necessary. This enhances the clarity and efficiency of the code.

Enhance exposure_spread function by refining replacement logic for players based on exposure targets. This update improves the handling of player replacements, ensuring that changes are only applied when necessary and maintaining clarity in the code structure.

Remove unnecessary break statement in exposure_spread function to enhance lineup modification logic and improve code clarity.

Refactor exposure_spread function to streamline lineup modification logic, ensuring that changes are applied correctly based on the current change counter and improving overall code clarity.

Refactor exposure_spread function to improve list comparison for comparable_player_list, ensuring clarity and consistency in checks for non-empty lists.

fixing exposure management

FLEX to UTIL for NHL stacking on FD

Refactor stacking logic in app.py to conditionally apply calculations for 'Stack' and 'Size' columns based on their existence in the DataFrame. This change improves code robustness and prevents potential errors during data processing.

Add MMA stacking configuration and enhance error handling in position export logic in app.py. This update introduces new player positions for MMA and improves the robustness of the data processing by adding exception handling for sorting and deduplication operations.

recent changes

Update NHL stacking configurations in app.py to include detailed player positions for Draftkings and Fanduel. This change enhances lineup flexibility and aligns with current game strategies.

updating for recent changes

Refactor stacking logic in app.py to streamline team assignment calculations for player lineups. This update removes legacy stacking code and optimizes the application of team data, enhancing performance and maintainability.

Refactor portfolio filtering in app.py to convert entries to string and remove invalid values before saving to compressed format. This update enhances data integrity and ensures only valid entries are retained.

Remove debug print statements from app.py that were used for validating the working frame structure and content. This cleanup enhances code readability and maintains focus on core functionality.

Add debug prints for working frame in app.py to assist with data validation. This change helps verify the structure and content of the working frame after applying salary cap filtering.

Reintroduce salary cap filtering for working frame in app.py to ensure only eligible players are loaded. This update enhances data integrity and aligns with previous optimizations for memory efficiency.

Update portfolio management in app.py to reintroduce salary cap filtering for working frame. This change ensures that only eligible players are loaded into the working frame, enhancing data integrity and memory efficiency.

Refactor portfolio storage in app.py to filter by salary cap before saving to compressed format. This change optimizes memory usage and ensures only eligible entries are retained.

Update score cutoff in chunk_name_matching function and add PowerShell scripts for instance management

Added QB force option for micro

Enhance Lineup Edge calculation in predict_dupes.py by normalizing the edge values based on the maximum and minimum edges. This adjustment improves the scaling of the Lineup Edge, leading to more accurate portfolio predictions.

ping

Correct Lineup Edge calculation in predict_dupes.py by changing the adjustment from addition to subtraction based on duplicate ownership. This modification refines the edge computation, improving the accuracy of portfolio predictions.

Update Lineup Edge calculation in predict_dupes.py to include a normalized adjustment based on duplicate ownership. This change improves the accuracy of the edge computation by factoring in the average number of duplicates, enhancing overall portfolio predictions.

Refactor similarity score calculation in predict_dupes.py by removing the reference to duplicate adjustments. This change clarifies the computation of player selection diversity while preserving the overall prediction model's integrity.

Refactor Lineup Edge calculation in predict_dupes.py by removing the duplicate adjustment and simplifying the formula. This change enhances clarity in the computation while maintaining the integrity of the prediction model.

ping

Refactor Lineup Edge calculation in predict_dupes.py to incorporate both raw edge and adjusted edge values. This change enhances the accuracy of the prediction model by averaging the adjusted edge with the raw edge, improving overall portfolio predictions.

Adjust Lineup Edge_Raw calculation in predict_dupes.py to include a constant offset, enhancing the accuracy of the edge computation for portfolio predictions.

Enhance freq_format in app.py to include 'Lineup Edge_Raw', improving data representation for portfolio management.

Update excluded_cols in app.py to include 'Lineup Edge_Raw', enhancing the data handling for portfolio management.